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Record W1863750193 · doi:10.1155/2006/192361

A Comparison of National Specialty Societies in Canada: Does CAG Measure Up?

2006· article· en· W1863750193 on OpenAlexaffvenueabout
William G. Paterson

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2006
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsQueen's University
Fundersnot available
KeywordsSpecialtyMeasure (data warehouse)Political scienceMedicineFamily medicineComputer scienceData mining

Abstract

fetched live from OpenAlex

The Federation of National Specialty Societies of Canada (FNSSC) is a formal organization established in 2003 that includes the majority of national medical and/or surgical specialty societies in Canada. Forty-two societies currently comprise the FNSSC, including the Canadian Association of Gastroenterology (CAG). One of the advantages of the FNSSC is that it provides a united voice on specialty concerns to the Canadian Medical Association and the Royal College of Physicians and Surgeons of Canada. Second, it affords a framework for sharing common problems and experiences among member societies, for example, regarding accreditation and funding. Third, its influence may prove useful in securing preferred contracts from suppliers, including hotel chains and insurance companies, for FNSSC associations. Information-sharing offers an interesting view to how various societies operate and the differences between them. As an example, Table 1 shows the categories and related annual fees for membership for 11 different FNSSC associations. The 11 associations represent organizations that participated in a recent FNSSC survey on annual conference fees (see below), and which illustrate the diversity of membership types and fees. Table 1 Membership categories and yearly fees for 11 Federation of National Specialty Societies of Canada

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.436

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.019
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.043
GPT teacher head0.358
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainEvaluation
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2006
Admission routes3
Has abstractyes

Explore more

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